





"""simple docstring"""






import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class 					__lowercase      (      _UpperCamelCase ,   unittest.TestCase				):







					'''simple docstring'''




					__lowerCAmelCase	=	LongformerTokenizer
					__lowerCAmelCase	=	True
					__lowerCAmelCase	=	LongformerTokenizerFast
					__lowerCAmelCase	=	True







					def 			_lowerCamelCase		(    self  ):
									super().setUp()

									# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
									__a			:    Dict      							=	[
									    '''l''',
									    '''o''',
									    '''w''',
									    '''e''',
									    '''r''',
									    '''s''',
									    '''t''',
									    '''i''',
									    '''d''',
									    '''n''',
									    '''\u0120''',
									    '''\u0120l''',
									    '''\u0120n''',
									    '''\u0120lo''',
									    '''\u0120low''',
									    '''er''',
									    '''\u0120lowest''',
									    '''\u0120newer''',
									    '''\u0120wider''',
									    '''<unk>''',
									]
									__a			:    List[str]      							=	dict(zip(_UpperCAmelCase						,      range(len(_UpperCAmelCase  )  )  )  )
									__a			:    Optional[int]      							=	['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
									__a			:    List[str]      							=	{'''unk_token''': '''<unk>'''}

									__a			:    Optional[Any]      							=	os.path.join(self.tmpdirname						,      VOCAB_FILES_NAMES['''vocab_file''']  )
									__a			:    str      							=	os.path.join(self.tmpdirname						,      VOCAB_FILES_NAMES['''merges_file''']  )
									with open(self.vocab_file						,      '''w'''						,      encoding='''utf-8'''  ) as fp:
													fp.write(json.dumps(_UpperCAmelCase  ) + '''\n'''  )
									with open(self.merges_file						,      '''w'''						,      encoding='''utf-8'''  ) as fp:
													fp.write('''\n'''.join(_UpperCAmelCase  )  )







					def 			_lowerCamelCase		(    self						,      **_UpperCAmelCase  ):
									kwargs.update(self.special_tokens_map  )
									return self.tokenizer_class.from_pretrained(self.tmpdirname						,      **_UpperCAmelCase  )







					def 			_lowerCamelCase		(    self						,      **_UpperCAmelCase  ):
									kwargs.update(self.special_tokens_map  )
									return self.rust_tokenizer_class.from_pretrained(self.tmpdirname						,      **_UpperCAmelCase  )







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase  ):
									__a			:    List[str]      							=	'''lower newer'''
									__a			:    List[Any]      							=	'''lower newer'''
									return input_text, output_text







					def 			_lowerCamelCase		(    self  ):
									__a			:    Dict      							=	self.tokenizer_class(self.vocab_file						,      self.merges_file						,      **self.special_tokens_map  )
									__a			:    Tuple      							=	'''lower newer'''
									__a			:    Dict      							=	['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
									__a			:    Union[str, Any]      							=	tokenizer.tokenize(_UpperCAmelCase  )  # , add_prefix_space=True)
									self.assertListEqual(_UpperCAmelCase						,      _UpperCAmelCase  )

									__a			:    Optional[int]      							=	tokens + [tokenizer.unk_token]
									__a			:    Any      							=	[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase  )						,      _UpperCAmelCase  )







					def 			_lowerCamelCase		(    self  ):
									__a			:    Tuple      							=	self.get_tokenizer()

									self.assertListEqual(tokenizer.encode('''Hello world!'''						,      add_special_tokens=_UpperCAmelCase  )						,      [0, 31414, 232, 328, 2]  )
									self.assertListEqual(
									    tokenizer.encode('''Hello world! cécé herlolip 418'''						,      add_special_tokens=_UpperCAmelCase  )						,      [0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]						,      )







					@slow
					def 			_lowerCamelCase		(    self  ):
									__a			:    Dict      							=	self.tokenizer_class.from_pretrained('''allenai/longformer-base-4096'''  )

									__a			:    Any      							=	tokenizer.encode('''sequence builders'''						,      add_special_tokens=_UpperCAmelCase  )
									__a			:    List[Any]      							=	tokenizer.encode('''multi-sequence build'''						,      add_special_tokens=_UpperCAmelCase  )

									__a			:    str      							=	tokenizer.encode(
									    '''sequence builders'''						,      add_special_tokens=_UpperCAmelCase						,      add_prefix_space=_UpperCAmelCase  )
									__a			:    Tuple      							=	tokenizer.encode(
									    '''sequence builders'''						,      '''multi-sequence build'''						,      add_special_tokens=_UpperCAmelCase						,      add_prefix_space=_UpperCAmelCase  )

									__a			:    Optional[Any]      							=	tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase  )
									__a			:    Dict      							=	tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase						,      _UpperCAmelCase  )

									assert encoded_sentence == encoded_text_from_decode
									assert encoded_pair == encoded_pair_from_decode







					def 			_lowerCamelCase		(    self  ):
									__a			:    Tuple      							=	self.get_tokenizer()

									__a			:    Union[str, Any]      							=	'''Encode this sequence.'''
									__a			:    List[str]      							=	tokenizer.byte_encoder[''' '''.encode('''utf-8'''  )[0]]

									# Testing encoder arguments
									__a			:    Any      							=	tokenizer.encode(_UpperCAmelCase						,      add_special_tokens=_UpperCAmelCase						,      add_prefix_space=_UpperCAmelCase  )
									__a			:    List[str]      							=	tokenizer.convert_ids_to_tokens(encoded[0]  )[0]
									self.assertNotEqual(_UpperCAmelCase						,      _UpperCAmelCase  )

									__a			:    Union[str, Any]      							=	tokenizer.encode(_UpperCAmelCase						,      add_special_tokens=_UpperCAmelCase						,      add_prefix_space=_UpperCAmelCase  )
									__a			:    int      							=	tokenizer.convert_ids_to_tokens(encoded[0]  )[0]
									self.assertEqual(_UpperCAmelCase						,      _UpperCAmelCase  )

									tokenizer.add_special_tokens({'''bos_token''': '''<s>'''}  )
									__a			:    List[str]      							=	tokenizer.encode(_UpperCAmelCase						,      add_special_tokens=_UpperCAmelCase  )
									__a			:    Optional[Any]      							=	tokenizer.convert_ids_to_tokens(encoded[1]  )[0]
									self.assertNotEqual(_UpperCAmelCase						,      _UpperCAmelCase  )

									# Testing spaces after special tokens
									__a			:    Dict      							=	'''<mask>'''
									tokenizer.add_special_tokens(
									    {'''mask_token''': AddedToken(_UpperCAmelCase						,      lstrip=_UpperCAmelCase						,      rstrip=_UpperCAmelCase  )}  )  # mask token has a left space
									__a			:    Any      							=	tokenizer.convert_tokens_to_ids(_UpperCAmelCase  )

									__a			:    Optional[Any]      							=	'''Encode <mask> sequence'''
									__a			:    Union[str, Any]      							=	'''Encode <mask>sequence'''

									__a			:    Tuple      							=	tokenizer.encode(_UpperCAmelCase  )
									__a			:    int      							=	encoded.index(_UpperCAmelCase  )
									__a			:    int      							=	tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]  )[0]
									self.assertEqual(_UpperCAmelCase						,      _UpperCAmelCase  )

									__a			:    Union[str, Any]      							=	tokenizer.encode(_UpperCAmelCase  )
									__a			:    Any      							=	encoded.index(_UpperCAmelCase  )
									__a			:    Any      							=	tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]  )[0]
									self.assertNotEqual(_UpperCAmelCase						,      _UpperCAmelCase  )







					def 			_lowerCamelCase		(    self  ):
									pass







					def 			_lowerCamelCase		(    self  ):
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""  ):
																	__a			:    Dict      							=	self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase						,      **_UpperCAmelCase  )
																	__a			:    List[Any]      							=	self.tokenizer_class.from_pretrained(_UpperCAmelCase						,      **_UpperCAmelCase  )
																	__a			:    Optional[Any]      							=	'''A, <mask> AllenNLP sentence.'''
																	__a			:    Tuple      							=	tokenizer_r.encode_plus(_UpperCAmelCase						,      add_special_tokens=_UpperCAmelCase						,      return_token_type_ids=_UpperCAmelCase  )
																	__a			:    Union[str, Any]      							=	tokenizer_p.encode_plus(_UpperCAmelCase						,      add_special_tokens=_UpperCAmelCase						,      return_token_type_ids=_UpperCAmelCase  )

																	# token_type_ids should put 0 everywhere
																	self.assertEqual(sum(tokens_r['''token_type_ids''']  )						,      sum(tokens_p['''token_type_ids''']  )  )

																	# attention_mask should put 1 everywhere, so sum over length should be 1
																	self.assertEqual(
																	    sum(tokens_r['''attention_mask''']  ) / len(tokens_r['''attention_mask''']  )						,      sum(tokens_p['''attention_mask''']  ) / len(tokens_p['''attention_mask''']  )						,      )

																	__a			:    int      							=	tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']  )
																	__a			:    Any      							=	tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']  )

																	# Rust correctly handles the space before the mask while python doesnt
																	self.assertSequenceEqual(tokens_p['''input_ids''']						,      [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]  )
																	self.assertSequenceEqual(tokens_r['''input_ids''']						,      [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]  )

																	self.assertSequenceEqual(
																	    _UpperCAmelCase						,      ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']  )
																	self.assertSequenceEqual(
																	    _UpperCAmelCase						,      ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']  )







					def 			_lowerCamelCase		(    self  ):
									for trim_offsets, add_prefix_space in itertools.product([True, False]						,      repeat=2  ):
													__a			:    List[str]      							=	self.rust_tokenizer_class.from_pretrained(
													    self.tmpdirname						,      use_fast=_UpperCAmelCase						,      add_prefix_space=_UpperCAmelCase						,      trim_offsets=_UpperCAmelCase  )

													__a			:    Optional[int]      							=	json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()  )
													__a			:    List[str]      							=	json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()  )

													self.assertEqual(pre_tokenizer_state['''add_prefix_space''']						,      _UpperCAmelCase  )

													self.assertEqual(post_processor_state['''add_prefix_space''']						,      _UpperCAmelCase  )
													self.assertEqual(post_processor_state['''trim_offsets''']						,      _UpperCAmelCase  )







					def 			_lowerCamelCase		(    self  ):
									# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
									# `trim_offsets`
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""  ):
																	__a			:    Any      							=	'''hello'''  # `hello` is a token in the vocabulary of `pretrained_name`
																	__a			:    Union[str, Any]      							=	f"""{text_of_1_token} {text_of_1_token}"""

																	__a			:    Union[str, Any]      							=	self.rust_tokenizer_class.from_pretrained(
																	    _UpperCAmelCase						,      use_fast=_UpperCAmelCase						,      add_prefix_space=_UpperCAmelCase						,      trim_offsets=_UpperCAmelCase  )
																	__a			:    List[Any]      							=	tokenizer_r(_UpperCAmelCase						,      return_offsets_mapping=_UpperCAmelCase						,      add_special_tokens=_UpperCAmelCase  )
																	self.assertEqual(encoding.offset_mapping[0]						,      (0, len(_UpperCAmelCase  ))  )
																	self.assertEqual(
																	    encoding.offset_mapping[1]						,      (len(_UpperCAmelCase  ) + 1, len(_UpperCAmelCase  ) + 1 + len(_UpperCAmelCase  ))						,      )

																	__a			:    Optional[Any]      							=	self.rust_tokenizer_class.from_pretrained(
																	    _UpperCAmelCase						,      use_fast=_UpperCAmelCase						,      add_prefix_space=_UpperCAmelCase						,      trim_offsets=_UpperCAmelCase  )
																	__a			:    Any      							=	tokenizer_r(_UpperCAmelCase						,      return_offsets_mapping=_UpperCAmelCase						,      add_special_tokens=_UpperCAmelCase  )
																	self.assertEqual(encoding.offset_mapping[0]						,      (0, len(_UpperCAmelCase  ))  )
																	self.assertEqual(
																	    encoding.offset_mapping[1]						,      (len(_UpperCAmelCase  ) + 1, len(_UpperCAmelCase  ) + 1 + len(_UpperCAmelCase  ))						,      )

																	__a			:    str      							=	self.rust_tokenizer_class.from_pretrained(
																	    _UpperCAmelCase						,      use_fast=_UpperCAmelCase						,      add_prefix_space=_UpperCAmelCase						,      trim_offsets=_UpperCAmelCase  )
																	__a			:    Any      							=	tokenizer_r(_UpperCAmelCase						,      return_offsets_mapping=_UpperCAmelCase						,      add_special_tokens=_UpperCAmelCase  )
																	self.assertEqual(encoding.offset_mapping[0]						,      (0, len(_UpperCAmelCase  ))  )
																	self.assertEqual(
																	    encoding.offset_mapping[1]						,      (len(_UpperCAmelCase  ), len(_UpperCAmelCase  ) + 1 + len(_UpperCAmelCase  ))						,      )

																	__a			:    List[Any]      							=	self.rust_tokenizer_class.from_pretrained(
																	    _UpperCAmelCase						,      use_fast=_UpperCAmelCase						,      add_prefix_space=_UpperCAmelCase						,      trim_offsets=_UpperCAmelCase  )
																	__a			:    Union[str, Any]      							=	tokenizer_r(_UpperCAmelCase						,      return_offsets_mapping=_UpperCAmelCase						,      add_special_tokens=_UpperCAmelCase  )
																	self.assertEqual(encoding.offset_mapping[0]						,      (0, len(_UpperCAmelCase  ))  )
																	self.assertEqual(
																	    encoding.offset_mapping[1]						,      (len(_UpperCAmelCase  ), len(_UpperCAmelCase  ) + 1 + len(_UpperCAmelCase  ))						,      )

																	__a			:    Optional[Any]      							=	f""" {text}"""

																	# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
																	#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
																	# )
																	# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
																	# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
																	# self.assertEqual(
																	#     encoding.offset_mapping[1],
																	#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
																	# )

																	__a			:    int      							=	self.rust_tokenizer_class.from_pretrained(
																	    _UpperCAmelCase						,      use_fast=_UpperCAmelCase						,      add_prefix_space=_UpperCAmelCase						,      trim_offsets=_UpperCAmelCase  )
																	__a			:    Tuple      							=	tokenizer_r(_UpperCAmelCase						,      return_offsets_mapping=_UpperCAmelCase						,      add_special_tokens=_UpperCAmelCase  )
																	self.assertEqual(encoding.offset_mapping[0]						,      (1, 1 + len(_UpperCAmelCase  ))  )
																	self.assertEqual(
																	    encoding.offset_mapping[1]						,      (1 + len(_UpperCAmelCase  ) + 1, 1 + len(_UpperCAmelCase  ) + 1 + len(_UpperCAmelCase  ))						,      )

																	__a			:    List[Any]      							=	self.rust_tokenizer_class.from_pretrained(
																	    _UpperCAmelCase						,      use_fast=_UpperCAmelCase						,      add_prefix_space=_UpperCAmelCase						,      trim_offsets=_UpperCAmelCase  )
																	__a			:    List[Any]      							=	tokenizer_r(_UpperCAmelCase						,      return_offsets_mapping=_UpperCAmelCase						,      add_special_tokens=_UpperCAmelCase  )
																	self.assertEqual(encoding.offset_mapping[0]						,      (0, 1 + len(_UpperCAmelCase  ))  )
																	self.assertEqual(
																	    encoding.offset_mapping[1]						,      (1 + len(_UpperCAmelCase  ), 1 + len(_UpperCAmelCase  ) + 1 + len(_UpperCAmelCase  ))						,      )

																	__a			:    List[str]      							=	self.rust_tokenizer_class.from_pretrained(
																	    _UpperCAmelCase						,      use_fast=_UpperCAmelCase						,      add_prefix_space=_UpperCAmelCase						,      trim_offsets=_UpperCAmelCase  )
																	__a			:    Union[str, Any]      							=	tokenizer_r(_UpperCAmelCase						,      return_offsets_mapping=_UpperCAmelCase						,      add_special_tokens=_UpperCAmelCase  )
																	self.assertEqual(encoding.offset_mapping[0]						,      (0, 1 + len(_UpperCAmelCase  ))  )
																	self.assertEqual(
																	    encoding.offset_mapping[1]						,      (1 + len(_UpperCAmelCase  ), 1 + len(_UpperCAmelCase  ) + 1 + len(_UpperCAmelCase  ))						,      )




'''simple docstring'''



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()
class 						lowerCamelCase_				(       snake_case_     ,					unittest.TestCase      ):
						_lowerCAmelCase  : str						  =					KandinskyInpaintPipeline
						_lowerCAmelCase  : List[Any]						  =					['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
						_lowerCAmelCase  : int						  =					[
						    'prompt',
						    'negative_prompt',
						    'image_embeds',
						    'negative_image_embeds',
						    'image',
						    'mask_image',
						]
						_lowerCAmelCase  : List[str]						  =					[
						    'generator',
						    'height',
						    'width',
						    'latents',
						    'guidance_scale',
						    'negative_prompt',
						    'num_inference_steps',
						    'return_dict',
						    'guidance_scale',
						    'num_images_per_prompt',
						    'output_type',
						    'return_dict',
						]
						_lowerCAmelCase  : Union[str, Any]						  =					False







						@property
						def        __lowercase     (	self :   str					):

													"""simple docstring"""
													return 32







						@property
						def        __lowercase     (	self :   str					):

													"""simple docstring"""
													return 32







						@property
						def        __lowercase     (	self :   Optional[int]					):

													"""simple docstring"""
													return self.time_input_dim







						@property
						def        __lowercase     (	self :   int					):

													"""simple docstring"""
													return self.time_input_dim * 4







						@property
						def        __lowercase     (	self :   int					):

													"""simple docstring"""
													return 1_00







						@property
						def        __lowercase     (	self :   Tuple					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''					)
													return tokenizer







						@property
						def        __lowercase     (	self :   List[Any]					):

													"""simple docstring"""
													torch.manual_seed(0					)
													SCREAMING_SNAKE_CASE       :   List[Any]         =      MCLIPConfig(
													    numDims=self.cross_attention_dim					,     transformerDimensions=self.text_embedder_hidden_size					,     hidden_size=self.text_embedder_hidden_size					,     intermediate_size=37					,     num_attention_heads=4					,     num_hidden_layers=5					,     vocab_size=10_05					,     )

													SCREAMING_SNAKE_CASE       :   Any         =      MultilingualCLIP(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      text_encoder.eval()

													return text_encoder







						@property
						def        __lowercase     (	self :   List[Any]					):

													"""simple docstring"""
													torch.manual_seed(0					)

													SCREAMING_SNAKE_CASE       :   Tuple         =      {
													    '''in_channels''': 9,
													    # Out channels is double in channels because predicts mean and variance
													    '''out_channels''': 8,
													    '''addition_embed_type''': '''text_image''',
													    '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
													    '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
													    '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
													    '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
													    '''layers_per_block''': 1,
													    '''encoder_hid_dim''': self.text_embedder_hidden_size,
													    '''encoder_hid_dim_type''': '''text_image_proj''',
													    '''cross_attention_dim''': self.cross_attention_dim,
													    '''attention_head_dim''': 4,
													    '''resnet_time_scale_shift''': '''scale_shift''',
													    '''class_embed_type''': None,
													}

													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      UNetaDConditionModel(**lowerCAmelCase__					)
													return model







						@property
						def        __lowercase     (	self :   Optional[Any]					):

													"""simple docstring"""
													return {
													    "block_out_channels": [32, 64],
													    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
													    "in_channels": 3,
													    "latent_channels": 4,
													    "layers_per_block": 1,
													    "norm_num_groups": 8,
													    "norm_type": "spatial",
													    "num_vq_embeddings": 12,
													    "out_channels": 3,
													    "up_block_types": [
													        "AttnUpDecoderBlock2D",
													        "UpDecoderBlock2D",
													    ],
													    "vq_embed_dim": 4,
													}







						@property
						def        __lowercase     (	self :   Union[str, Any]					):

													"""simple docstring"""
													torch.manual_seed(0					)
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      VQModel(**self.dummy_movq_kwargs					)
													return model







						def        __lowercase     (	self :   Optional[int]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Any         =      self.dummy_text_encoder
													SCREAMING_SNAKE_CASE       :   Tuple         =      self.dummy_tokenizer
													SCREAMING_SNAKE_CASE       :   int         =      self.dummy_unet
													SCREAMING_SNAKE_CASE       :   str         =      self.dummy_movq

													SCREAMING_SNAKE_CASE       :   Optional[int]         =      DDIMScheduler(
													    num_train_timesteps=10_00					,     beta_schedule='''linear'''					,     beta_start=0.0_0085					,     beta_end=0.012					,     clip_sample=lowerCAmelCase__					,     set_alpha_to_one=lowerCAmelCase__					,     steps_offset=1					,     prediction_type='''epsilon'''					,     thresholding=lowerCAmelCase__					,     )

													SCREAMING_SNAKE_CASE       :   Optional[int]         =      {
													    '''text_encoder''': text_encoder,
													    '''tokenizer''': tokenizer,
													    '''unet''': unet,
													    '''scheduler''': scheduler,
													    '''movq''': movq,
													}

													return components







						def        __lowercase     (	self :   Dict					,     lowerCAmelCase__ :   Optional[Any]					,     lowerCAmelCase__ :   List[Any]=0					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      floats_tensor((1, self.cross_attention_dim)					,     rng=random.Random(lowerCAmelCase__					)					).to(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   List[Any]         =      floats_tensor((1, self.cross_attention_dim)					,     rng=random.Random(seed + 1					)					).to(lowerCAmelCase__					)
													# create init_image
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      floats_tensor((1, 3, 64, 64)					,     rng=random.Random(lowerCAmelCase__					)					).to(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Tuple         =      image.cpu().permute(0					,     2					,     3					,     1					)[0]
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      Image.fromarray(np.uinta(lowerCAmelCase__					)					).convert('''RGB'''					).resize((2_56, 2_56)					)
													# create mask
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      np.ones((64, 64)					,     dtype=np.floataa					)
													SCREAMING_SNAKE_CASE       :   str         =      0

													if str(lowerCAmelCase__					).startswith('''mps'''					):
																				SCREAMING_SNAKE_CASE       :   Tuple         =      torch.manual_seed(lowerCAmelCase__					)
													else:
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      torch.Generator(device=lowerCAmelCase__					).manual_seed(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Tuple         =      {
													    '''prompt''': '''horse''',
													    '''image''': init_image,
													    '''mask_image''': mask,
													    '''image_embeds''': image_embeds,
													    '''negative_image_embeds''': negative_image_embeds,
													    '''generator''': generator,
													    '''height''': 64,
													    '''width''': 64,
													    '''num_inference_steps''': 2,
													    '''guidance_scale''': 4.0,
													    '''output_type''': '''np''',
													}
													return inputs







						def        __lowercase     (	self :   List[Any]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Any         =      '''cpu'''

													SCREAMING_SNAKE_CASE       :   Dict         =      self.get_dummy_components()

													SCREAMING_SNAKE_CASE       :   Optional[int]         =      self.pipeline_class(**lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   int         =      pipe.to(lowerCAmelCase__					)

													pipe.set_progress_bar_config(disable=lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   str         =      pipe(**self.get_dummy_inputs(lowerCAmelCase__					)					)
													SCREAMING_SNAKE_CASE       :   int         =      output.images

													SCREAMING_SNAKE_CASE       :   int         =      pipe(
													    **self.get_dummy_inputs(lowerCAmelCase__					)					,     return_dict=lowerCAmelCase__					,     )[0]

													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      image[0, -3:, -3:, -1]
													SCREAMING_SNAKE_CASE       :   Dict         =      image_from_tuple[0, -3:, -3:, -1]

													print(F"""image.shape {image.shape}"""					)

													assert image.shape == (1, 64, 64, 3)

													SCREAMING_SNAKE_CASE       :   List[str]         =      np.array(
													    [0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786]					)

													assert (
													    np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2
													), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
													assert (
													    np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1e-2
													), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""







						def        __lowercase     (	self :   str					):

													"""simple docstring"""
													super().test_inference_batch_single_identical(expected_max_diff=3e-3					)


@slow
@require_torch_gpu
class 						lowerCamelCase_				(       unittest.TestCase      ):







						def        __lowercase     (	self :   List[str]					):

													"""simple docstring"""
													# clean up the VRAM after each test
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()







						def        __lowercase     (	self :   Dict					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   List[Any]         =      load_numpy(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
													    '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'''					)

													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''					)
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      np.ones((7_68, 7_68)					,     dtype=np.floataa					)
													SCREAMING_SNAKE_CASE       :   List[str]         =      0

													SCREAMING_SNAKE_CASE       :   Dict         =      '''a hat'''

													SCREAMING_SNAKE_CASE       :   str         =      KandinskyPriorPipeline.from_pretrained(
													    '''kandinsky-community/kandinsky-2-1-prior'''					,     torch_dtype=torch.floataa					)
													pipe_prior.to(lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      KandinskyInpaintPipeline.from_pretrained(
													    '''kandinsky-community/kandinsky-2-1-inpaint'''					,     torch_dtype=torch.floataa					)
													SCREAMING_SNAKE_CASE       :   Dict         =      pipeline.to(lowerCAmelCase__					)
													pipeline.set_progress_bar_config(disable=lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   Optional[int]         =      torch.Generator(device='''cpu'''					).manual_seed(0					)
													SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   Any         =      pipe_prior(
													    lowerCAmelCase__					,     generator=lowerCAmelCase__					,     num_inference_steps=5					,     negative_prompt=''''''					,     ).to_tuple()

													SCREAMING_SNAKE_CASE       :   Tuple         =      pipeline(
													    lowerCAmelCase__					,     image=lowerCAmelCase__					,     mask_image=lowerCAmelCase__					,     image_embeds=lowerCAmelCase__					,     negative_image_embeds=lowerCAmelCase__					,     generator=lowerCAmelCase__					,     num_inference_steps=1_00					,     height=7_68					,     width=7_68					,     output_type='''np'''					,     )

													SCREAMING_SNAKE_CASE       :   Tuple         =      output.images[0]

													assert image.shape == (7_68, 7_68, 3)

													assert_mean_pixel_difference(lowerCAmelCase__					,     lowerCAmelCase__					)




